In this paper, we propose QuantMiner, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers "good" intervals in association rules by optimizing both the support and the confidence. The experiments on real and artificial databases have shown the usefulness of QuantMiner as an interactive data mining tool. Keywords: Association rules, quantitative (numeric) attributes, unsupervised discretization, genetic algorithm

Authors

• Ansaf Salleb-Aouissi
• Christel Vrain
• Cyril Nortet

Keywords

• Association rules
• quantitative (numeric) attributes
• unsupervised discretization
• genetic algorithm